ToolBattleAI

Problem: AI Hallucinations & Inaccurate Information Large language models (LLMs) sometimes produce plausible-sounding but false or fabricated answers (“hallucinations”). This is the root of many real-world harms — bad medical advice, bogus citations, bad investment tips, legal mistakes. Below is a comprehensive, step-by-step solution you can implement as a creator, product owner, or engineer. It covers quick mitigations, production architecture (RAG + verification), UX, testing & monitoring, and governance. Quick mitigation (do this first, in hours) 1. Label uncertainty in the UI. If the model replies to factual queries, show a confidence indicator: “Confidence: Low / Medium / High” (computed by downstream classifier or heuristics). 2. Require citations for facts. For any claim (facts, numbers, dates, medical/financial/legal), require the model to include at least one source link. If none, show a warning. 3. Add a “Verify” CTA. Let users click “Verify this answer...